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Proceedings Paper

Automated polarization-discrimination technique to minimize lidar-detected skylight background noise
Author(s): Yasser Y. Hassebo; Samir Ahmed
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Paper Abstract

Recently, there has been significant interest in lidar signal-to-noise ratio (SNR) improvements, particularly for lidar daytime operations. Previously, we devised in the remote sensing laboratory at the City College of New York a polarization discrimination technique to maximize lidar detected SNR taking advantage of the natural polarization properties of scattered skylight radiation to track and minimize detected sky background signal (BGS). This tracking technique was achieved by rotating, manually, a combination of polarizer and analyzer on both the lidar transmitter and receiver subsystems, respectively. The polarization orientation at which the minimum BGS occurs, follows the solar azimuth angle, even for high aerosol loading. This has been confirmed, in our previous work, both theoretically, assuming single scattering theory, and experimentally. In this paper, a design to automate the polarization discrimination technique by real time tracking of the azimuth angle to attain the minimum BGS is presented. We introduce a feedback control system to track the minimum BGS by rotating the detector analyzer and the transmission polarizer simultaneously to maximize the SNR and attainable lidar ranges, thus achieving the same results as would be done manually. Analytical results for New York City are summarized and an approach for applying the proposed design globally is investigated.

Paper Details

Date Published: 27 October 2007
PDF: 12 pages
Proc. SPIE 6750, Lidar Technologies, Techniques, and Measurements for Atmospheric Remote Sensing III, 675009 (27 October 2007); doi: 10.1117/12.738191
Show Author Affiliations
Yasser Y. Hassebo, LaGuardia Community College/CUNY (United States)
The City College/CUNY (United States)
Samir Ahmed, The City College/CUNY (United States)

Published in SPIE Proceedings Vol. 6750:
Lidar Technologies, Techniques, and Measurements for Atmospheric Remote Sensing III
Upendra N. Singh; Gelsomina Pappalardo, Editor(s)

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